P2 (WIP): raw X/Y assembly, canonical Parquet model, simurg ingest

- signal/raw_xy.py: vectorised Infinium I/II raw X/Y and control assembly
  (plan §7.1) with a per-chip address-map cache; IN-027 coverage gate
- core/schemas.py: Arrow schemas (loci, sample_intensity, controls),
  deterministic atomic Parquet writing
- pipeline/ingest.py + CLI: discovery, sheet, IN-010 naming, overwrite
  check before any work, spawn-based workers, per-sample isolation,
  --resume by input hashes, run manifest, exit codes 0/3/4
- matches gtc2vcf raw X/Y and controls exactly on 18 GSA samples;
  outputs byte-identical across thread counts and layouts

Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv

src/simurg/formats/bpm.py

@@ -25,7 +25,7 @@ Normalization-bin rule (plan §6.2)::
 from __future__ import annotations
 
 import struct
-from dataclasses import dataclass, field
+from dataclasses import dataclass, field, replace
 from pathlib import Path
 from typing import Any
 
@@ -103,6 +103,37 @@ class Manifest:
     def name_index(self) -> dict[str, int]:
         return {n: i for i, n in enumerate(self.names)}
 
+    def lite(self) -> Manifest:
+        """Copy keeping only what intensity assembly needs (cheap to pickle to worker processes)."""
+        n = self.num_loci
+        empty = [""] * 0
+        return replace(
+            self,
+            names=list(self.names),
+            ilmn_ids=empty,
+            snps=empty,
+            chroms=empty,
+            ilmn_strands=empty,
+            source_strands=empty,
+            ref_strands=empty,
+            ploidies=empty,
+            species=empty,
+            genome_builds=empty,
+            sources=empty,
+            source_versions=empty,
+            customer_strands=empty,
+            source_seqs=empty,
+            top_genomic_seqs=empty,
+            allele_a_probe_seqs=empty,
+            allele_b_probe_seqs=empty,
+            map_infos=np.zeros(0, np.int64),
+            base_fractions=None,
+            header_lines=[],
+            heading={},
+            exp_clusters=self.exp_clusters[:n],
+            intensity_only=self.intensity_only[:n],
+        )
+
     def summary(self) -> dict[str, Any]:
         at = self.assay_types
         return {